[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83109-en":3,"doc-seo-83109-105":29,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83109,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","AVLM-Enhanced Framework for Comprehensive Traffic Sign Condition Assessment Integrating Daytime Visual Performance and Nighttime Retroreflectivity Evaluation","Traffic signs underpin road safety by communicating guidance and regulation across lighting conditions, yet current MUTCD-focused evaluations are costly and labor-intensive. This study proposes an integrated daytime–nighttime assessment framework combining fine-tuned vision language models for legibility, color, surface/shape integrity, and surrounding environment, with LiDAR-derived nighttime retroreflectivity calibrated to established procedures. Model outputs are converted to quantitative scores and aggregated into a Sign Condition Index for maintenance prioritization. Experimental evaluation on 462 validated signs shows strong performance and flags 68 signs for immediate replacement due to inadequate retroreflectivity.","AVLM-Enhanced Framework for Comprehensive Traffic Sign Condition Assessment Integrating Daytime Visual Performance and Nighttime Retroreflectivity Evaluation  \nLinlin Zhang  \nPost Doctoral Fellow  \nDepartment of Civil and Environmental Engineering University of Missouri-Columbia, Columbia, MO, USA, 65201 Email: [linlinzhang@missouri.edu](linlinzhang@missouri.edu)  \nNeema Jakisa Owor  \nPh.D. Candidate  \nDepartment of Civil and Environmental Engineering University of Missouri-Columbia, Columbia, MO, USA, 65201 [Email: nodyv@missouri.edu](Email: nodyv@missouri.edu)  \nXiang Yu  \nPh.D. Student  \nDepartment of Civil and Environmental Engineering University of Missouri-Columbia, Columbia, MO, USA, 65201 [Email: xytm4@missouri.edu](Email: xytm4@missouri.edu)  \nAbby Watts  \nMaster Student  \nDepartment of Civil and Environmental Engineering University of Missouri-Columbia, Columbia, MO, USA, 65201 Email: [arw5dv@missouri.edu](arw5dv@missouri.edu)  \n[Yaw Adu-Gyamfi](Yaw Adu-Gyamfi)  \nAssociate Professor  \nDepartment of Civil and Environmental Engineering University of Missouri-Columbia, Columbia, MO, USA, 65201 Email: [adugyamfiy@missouri.edu](adugyamfiy@missouri.edu)  \nWord Count: 6, 173 words + 5 table (1250) = 7,423 words  \nSubmitted for consideration for presentation at the 105th Annual Meeting of the Transportation Research Board, January 2026  \nSubmitted Date: August 1, 2025  \nZhang, Owor, Yu, Watts, andAdu-Gyamfi  \nABSTRACT  \nTraffic signs are crucial components of road safety, serving as visual tools under all lighting conditions. The Manual on Uniform Traffic Control Devices (MUTCD) specifies daytime visual factors such as legibility and color contrast, and nighttime retroreflectivity requirements. Traditional assessment methods rely on manual inspections, which the Federal Highway Administration (FHWA) notes are subjective, labor-intensive and pose safety concerns, while retroreflectometers are expensive and unaffordable for smaller agencies. Most existing studies focus on either daytime factors or nighttime retroreflectivity but rarely integrate both aspects comprehensively. This study developsa novel framework that systematically evaluates traffic signs through integrated daytime-nighttime assessment. The methodology employs three fine-tuned Vision Language Models (VLMs) for daytime visual performance assessment across four key factors: legibility, color, surface and shape integrity, and surrounding environment conditions. VLM predictions are converted to numerical scores through sentiment analysis and Contrastive Language-Image Pre-Training (CLIP) scoring, while nighttime performance is assessed using LiDAR-derived retroreflectivity following established calibration procedures. The framework integrates these components into a comprehensive Sign Condition Index (SCI) for maintenance guidance. Evaluation results demonstrated that LLaVA and Qwen outperformed InternVL, achieving bidirectional cosine similarity scores of 0.67-0.76 across all factors. Among 462 validated traffic signs, 68 were flagged by the proposed framework as requiring immediate replacement due to inadequate retroreflectivity performance. This research provides a cost-effective alternative to traditional manual inspections for comprehensive traffic sign condition assessment.  \nKeywords: Traffic Sign Assessment, Vision Language Models, Retroreflectivity, MUTCD Compliance, Transportation Infrastructure Management, LiDAR Retro-Intensity  \nZhang, Owor, Yu, Watts, andAdu-Gyamfi  \nINTRODUCTION  \nCities across the United States maintain extensive inventories of traffic signs, with evaluations for Manual on Uniform Traffic Control Devices (MUTCD) (1) compliance being resource-intensive and costly, particularly straining smaller municipal budgets. Traffic signs are crucial components of road safety and traffic management, serving as visual tools to guide and regulate drivers, pedestrians and cyclists under all lighting conditions. 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